The present invention relates to image processing in computer vision. More specifically it relates to template matching in computer vision.
Template matching is a fundamental operator in computer vision and is widely used in feature tracking, motion estimation, image alignment, and mosaicing. Under a certain parameterized warping model, the traditional template matching algorithm estimates the geometric warp parameters that minimize the SSD (sum of square of differences) between the target and a warped template.
The performance of the template matching can be characterized by deriving the distribution of warp parameter estimate as a function of the ideal template, the ideal warp parameters, and a given noise or perturbation model.
The performance of template matching depends on the choice or block size of the template. Accordingly methods to optimize the choice or block size of the template are required.